1. Field of the Invention
The present invention relates generally to computer vision, and more particularly, to video based detection of specific events, such as a fall-down event.
2. Prior Art
Video based event detection has been approached in a number of ways in the prior art. One such way has been to analyze object trajectories to extract events like “person entered/exited” or “person deposited an object.” Simple motions such as “person walking” or “person running” have been learned and recognized from spatio-temporal motion templates. Example representations include motion history images, which capture recent object motion, and optical flow measurements. Probabilistic techniques such as Hidden Markov models (HMMs) and Bayesian nets have also been used extensively to recognize complex motion patterns and to learn and recognize human activities. Furthermore, invariance to changes in viewpoint has been studied and action recognition from multiple viewpoints has been analyzed in the prior art.
In the current state of the art, simple events (person entering a room) can be recognized well and without constraints. More complicated events/motions (person sitting on a chair) can either be recognized from a single viewpoint, or from multiple representations for multiple views of the event. However, current systems do not address the large amount of variation in appearance of certain events, such as a person falling down. That is, similar events, which can be performed in a great number of ways, cannot be consistently detected by the methods of the prior art. Other events that can be similarly classified include, but are not limited to, staggering and wild (panic) gestures (which can also be a visual way of calling for help).
Therefore, there is a need in the art for a method and apparatus for consistently detecting such events.